# source: https://raw.githubusercontent.com/genhack/ft_my/eb7a02ae3b075c1024ec9ce87f49b02d7e0c5d80/new_test.py
# --- Do not remove these libs ---
from freqtrade.strategy import IStrategy, merge_informative_pair
from pandas import DataFrame
import talib.abstract as ta
import logging
import freqtrade.vendor.qtpylib.indicators as qtpylib

# --------------------------------
import pandas as pd
import numpy as np
import technical.indicators as ftt
from freqtrade.exchange import timeframe_to_minutes
from technical.util import resample_to_interval, resampled_merge

logger = logging.getLogger(__name__)

def pivots_points(dataframe: pd.DataFrame, timeperiod=1, levels=4) -> pd.DataFrame:
   
    data = {}

    low = qtpylib.rolling_mean(
        series=pd.Series(index=dataframe.index, data=dataframe["low"]), window=timeperiod
    )

    high = qtpylib.rolling_mean(
        series=pd.Series(index=dataframe.index, data=dataframe["high"]), window=timeperiod
    )

    # Pivot WIP Genhack 13022021.
    data["pivot"] = qtpylib.rolling_mean(series=qtpylib.typical_price(dataframe), window=timeperiod)

    # Resistance #1
    # data["r1"] = (2 * data["pivot"]) - low ... Standard
    # R1 = PP + 0.382 * (HIGHprev - LOWprev) ... fibonacci
    data["r1"] = data['pivot'] + 0.382 * (high - low)

    data["rS1"] = data['pivot'] + 0.0955 * (high - low)


    # Resistance #2
    # data["s1"] = (2 * data["pivot"]) - high ... Standard
    # S1 = PP - 0.382 * (HIGHprev - LOWprev) ... fibonacci
    data["s1"] = data["pivot"] - 0.382 * (high - low)

    # Calculate Resistances and Supports >1
    for i in range(2, levels + 1):
        prev_support = data["s" + str(i - 1)]
        prev_resistance = data["r" + str(i - 1)]

        # Resitance
        data["r" + str(i)] = (data["pivot"] - prev_support) + prev_resistance

        # Support
        data["s" + str(i)] = data["pivot"] - (prev_resistance - prev_support)

    return pd.DataFrame(index=dataframe.index, data=data)


def create_ichimoku(dataframe, conversion_line_period, displacement, base_line_periods, laggin_span):
    ichimoku = ftt.ichimoku(dataframe,
                            conversion_line_period=conversion_line_period,
                            base_line_periods=base_line_periods,
                            laggin_span=laggin_span,
                            displacement=displacement
                            )
    dataframe[f'tenkan_sen_{conversion_line_period}'] = ichimoku['tenkan_sen']
    dataframe[f'kijun_sen_{conversion_line_period}'] = ichimoku['kijun_sen']
    dataframe[f'senkou_a_{conversion_line_period}'] = ichimoku['senkou_span_a']
    dataframe[f'senkou_b_{conversion_line_period}'] = ichimoku['senkou_span_b']
    return dataframe #<-Thanks Blood4rc


class github_genhack_ft_my__new_test__20220213_212105(IStrategy):

    # Optimal timeframe for the strategy
    timeframe = '5m'

    # generate signals from the 1h timeframe
    informative_timeframe = '1h'

    # WARNING: ichimoku is a long indicator, if you remove or use a
    # shorter startup_candle_count your results will be unstable/invalid
    # for up to a week from the start of your backtest or dry/live run
    # (180 candles = 7.5 days)
    startup_candle_count = 444  # MAXIMUM ICHIMOKU

    # NOTE: this strat only uses candle information, so processing between
    # new candles is a waste of resources as nothing will change
    process_only_new_candles = True

    minimal_roi = {
        "0": 10 #Better Close less then 10%
    }
    
    plot_config = {
        'main_plot': {
            'rS1_1h': {'color': 'blue'},
            'senkou_b_444': {'color': 'grey'},
            'kijun_sen_355': {'color': 'blue'},
            'tenkan_sen_355': {'color': 'red'},
            'senkou_a_100': {'color': 'orange'},
            'senkou_b_100': {'color': 'brown'},
            'tenkan_sen_9': {'color': 'black'},
            
        },
        'subplots': {
            'MACD': {
                'macd_1h': {'color': 'blue'},
                'macdsignal_1h': {'color': 'orange'},
            },
        }
    }

    # WARNING setting a stoploss for this strategy doesn't make much sense, as it will buy
    # back into the trend at the next available opportunity, unless the trend has ended,
    # in which case it would sell anyway.

    # Stoploss:
    stoploss = -0.10

    def informative_pairs(self):
        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, self.informative_timeframe) for pair in pairs]
        if self.dp:
            for pair in pairs:
                informative_pairs += [(pair, "1h")]
        return informative_pairs

    def slow_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        """
        # dataframe "1d, 5m"
        """

        dataframe1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1h")
        #dataframe5m = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="5m")

        # Pivots Points
        pp = pivots_points(dataframe1h)
        dataframe1h['pivot'] = pp['pivot']
        dataframe1h['r1'] = pp['r1']
        dataframe1h['s1'] = pp['s1']
        dataframe1h['rS1'] = pp['rS1']
        # Pivots Points
        dataframe = merge_informative_pair(dataframe, dataframe1h, self.timeframe, "1h", ffill=True)

        """
        # dataframe normal
        """
        
        create_ichimoku(dataframe, conversion_line_period=9, displacement=26, base_line_periods=26, laggin_span=52)    
        create_ichimoku(dataframe, conversion_line_period=20, displacement=88, base_line_periods=88, laggin_span=88)
        create_ichimoku(dataframe, conversion_line_period=100, displacement=88, base_line_periods=440, laggin_span=440)
        create_ichimoku(dataframe, conversion_line_period=355, displacement=800, base_line_periods=155, laggin_span=155)
        create_ichimoku(dataframe, conversion_line_period=444, displacement=444, base_line_periods=444, laggin_span=444)
        #Ema Control for buy and sell on t5. IF ema 5 cros the kinj sen 9 buy. for sell we need ema 10.
      

        dataframe['catch'] = (
            (dataframe['kijun_sen_355'] >= dataframe['tenkan_sen_355']) &
            (dataframe['senkou_b_100'] > dataframe['senkou_a_100']) &
            (dataframe['tenkan_sen_9'] >= dataframe['senkou_b_100']) &
            (dataframe['close'] > dataframe['rS1']) 
        ).astype('int')
         
        dataframe['trending_over'] = (
            (dataframe['senkou_b_444'] <= dataframe['close'])   
        ).astype('int') * 2
        return dataframe
   
    def fast_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # none atm
        return dataframe
      
    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
      
        dataframe = self.slow_tf_indicators(dataframe, metadata)
      
        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (dataframe['catch'] > 0)
            & (dataframe['trending_over'] <= 0)
            , 'buy'] = 1
        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (dataframe['trending_over'] > 0)
            , 'sell'] = 1
        return dataframe